Overview

Dataset statistics

Number of variables28
Number of observations85
Missing cells33
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 KiB
Average record size in memory225.5 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-09" Constant
url has a high cardinality: 85 distinct values High cardinality
name has a high cardinality: 79 distinct values High cardinality
_embedded_show_url has a high cardinality: 65 distinct values High cardinality
_embedded_show_name has a high cardinality: 65 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 57 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 57 distinct values High cardinality
_embedded_show_summary has a high cardinality: 54 distinct values High cardinality
_links_self_href has a high cardinality: 85 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with season and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with number and 2 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 12 other fieldsHigh correlation
summary is highly correlated with url and 3 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_summary and 7 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
name is highly correlated with url and 2 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
type is highly correlated with summary and 8 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 7 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
id is highly correlated with url and 12 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 22 other fieldsHigh correlation
season is highly correlated with url and 13 other fieldsHigh correlation
number is highly correlated with url and 15 other fieldsHigh correlation
type is highly correlated with url and 14 other fieldsHigh correlation
airtime is highly correlated with id and 19 other fieldsHigh correlation
airstamp is highly correlated with id and 21 other fieldsHigh correlation
runtime is highly correlated with url and 19 other fieldsHigh correlation
summary is highly correlated with url and 6 other fieldsHigh correlation
_embedded_show_id is highly correlated with url and 19 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with url and 20 other fieldsHigh correlation
_embedded_show_language is highly correlated with url and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 15 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with url and 18 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 10 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 12 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
number has 4 (4.7%) missing values Missing
runtime has 4 (4.7%) missing values Missing
_embedded_show_runtime has 22 (25.9%) missing values Missing
_embedded_show_averageRuntime has 3 (3.5%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_embedded_show_url is uniformly distributed Uniform
_embedded_show_name is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:04:48.137360
Analysis finished2022-05-10 02:05:24.644859
Duration36.51 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023986.129
Minimum1941971
Maximum2318101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:24.844964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1941971
5-th percentile1965909
Q11977639
median1985044
Q32041156
95-th percentile2190514.6
Maximum2318101
Range376130
Interquartile range (IQR)63517

Descriptive statistics

Standard deviation77178.74464
Coefficient of variation (CV)0.03813205215
Kurtosis2.23269812
Mean2023986.129
Median Absolute Deviation (MAD)9798
Skewness1.709983403
Sum172038821
Variance5956558625
MonotonicityNot monotonic
2022-05-09T21:05:25.012406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21796121
 
1.2%
19761581
 
1.2%
19854631
 
1.2%
19849491
 
1.2%
19849481
 
1.2%
19776391
 
1.2%
19776381
 
1.2%
19761941
 
1.2%
19761931
 
1.2%
19761591
 
1.2%
Other values (75)75
88.2%
ValueCountFrequency (%)
19419711
1.2%
19451451
1.2%
19588661
1.2%
19600331
1.2%
19644941
1.2%
19715691
1.2%
19719501
1.2%
19726431
1.2%
19726441
1.2%
19726451
1.2%
ValueCountFrequency (%)
23181011
1.2%
22059711
1.2%
22059701
1.2%
21954121
1.2%
21926231
1.2%
21820811
1.2%
21796121
1.2%
21748991
1.2%
21661941
1.2%
21539171
1.2%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://www.tvmaze.com/episodes/2179612/kontakty-1x29-kontakty-v-telefone-nikolaa-soboleva-klava-koka-eldar-dzarahov-andrej-malahov-edvard-bil
 
1
https://www.tvmaze.com/episodes/1976158/new-face-1x17-episode-17
 
1
https://www.tvmaze.com/episodes/1985463/you-complete-me-1x07-episode-7
 
1
https://www.tvmaze.com/episodes/1984949/dream-detective-1x10-episode-10
 
1
https://www.tvmaze.com/episodes/1984948/dream-detective-1x09-episode-9
 
1
Other values (80)
80 

Length

Max length149
Median length97
Mean length81.36470588
Min length57

Characters and Unicode

Total characters6916
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2179612/kontakty-1x29-kontakty-v-telefone-nikolaa-soboleva-klava-koka-eldar-dzarahov-andrej-malahov-edvard-bil
2nd rowhttps://www.tvmaze.com/episodes/1986871/kotiki-1x08-seria-8
3rd rowhttps://www.tvmaze.com/episodes/1983257/mertvye-dusi-1x01-seria-1
4th rowhttps://www.tvmaze.com/episodes/1983258/mertvye-dusi-1x02-seria-2
5th rowhttps://www.tvmaze.com/episodes/1971569/mermaid-prince-2x09-episode-9

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179612/kontakty-1x29-kontakty-v-telefone-nikolaa-soboleva-klava-koka-eldar-dzarahov-andrej-malahov-edvard-bil1
 
1.2%
https://www.tvmaze.com/episodes/1976158/new-face-1x17-episode-171
 
1.2%
https://www.tvmaze.com/episodes/1985463/you-complete-me-1x07-episode-71
 
1.2%
https://www.tvmaze.com/episodes/1984949/dream-detective-1x10-episode-101
 
1.2%
https://www.tvmaze.com/episodes/1984948/dream-detective-1x09-episode-91
 
1.2%
https://www.tvmaze.com/episodes/1977639/to-love-1x28-episode-281
 
1.2%
https://www.tvmaze.com/episodes/1977638/to-love-1x27-episode-271
 
1.2%
https://www.tvmaze.com/episodes/1976194/psych-hunter-1x28-episode-281
 
1.2%
https://www.tvmaze.com/episodes/1976193/psych-hunter-1x27-episode-271
 
1.2%
https://www.tvmaze.com/episodes/1976159/new-face-1x18-episode-181
 
1.2%
Other values (75)75
88.2%

Length

2022-05-09T21:05:25.194586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179612/kontakty-1x29-kontakty-v-telefone-nikolaa-soboleva-klava-koka-eldar-dzarahov-andrej-malahov-edvard-bil1
 
1.2%
https://www.tvmaze.com/episodes/1983584/la-vida-moderna-7x53-el-programa-de-la-ilacion1
 
1.2%
https://www.tvmaze.com/episodes/1983257/mertvye-dusi-1x01-seria-11
 
1.2%
https://www.tvmaze.com/episodes/1983258/mertvye-dusi-1x02-seria-21
 
1.2%
https://www.tvmaze.com/episodes/1971569/mermaid-prince-2x09-episode-91
 
1.2%
https://www.tvmaze.com/episodes/1985044/wan-sheng-jie-2x11-enough-money-was-left-for-this-episode1
 
1.2%
https://www.tvmaze.com/episodes/1985616/yi-nian-yong-heng-1x20-episode-201
 
1.2%
https://www.tvmaze.com/episodes/2096296/no-turning-back-romance-1x02-21
 
1.2%
https://www.tvmaze.com/episodes/2030019/dolls-frontline-2x11-episode-111
 
1.2%
https://www.tvmaze.com/episodes/2066368/chu-feng-yi-dian-shizi-1x05-episode-51
 
1.2%
Other values (75)75
88.2%

Most occurring characters

ValueCountFrequency (%)
e590
 
8.5%
-534
 
7.7%
s449
 
6.5%
/425
 
6.1%
t420
 
6.1%
o365
 
5.3%
a299
 
4.3%
w276
 
4.0%
m255
 
3.7%
i255
 
3.7%
Other values (30)3048
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4748
68.7%
Decimal Number954
 
13.8%
Other Punctuation680
 
9.8%
Dash Punctuation534
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e590
12.4%
s449
 
9.5%
t420
 
8.8%
o365
 
7.7%
a299
 
6.3%
w276
 
5.8%
m255
 
5.4%
i255
 
5.4%
p255
 
5.4%
d188
 
4.0%
Other values (16)1396
29.4%
Decimal Number
ValueCountFrequency (%)
1216
22.6%
2124
13.0%
0122
12.8%
9117
12.3%
369
 
7.2%
866
 
6.9%
764
 
6.7%
464
 
6.7%
656
 
5.9%
556
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/425
62.5%
.170
 
25.0%
:85
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4748
68.7%
Common2168
31.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e590
12.4%
s449
 
9.5%
t420
 
8.8%
o365
 
7.7%
a299
 
6.3%
w276
 
5.8%
m255
 
5.4%
i255
 
5.4%
p255
 
5.4%
d188
 
4.0%
Other values (16)1396
29.4%
Common
ValueCountFrequency (%)
-534
24.6%
/425
19.6%
1216
10.0%
.170
 
7.8%
2124
 
5.7%
0122
 
5.6%
9117
 
5.4%
:85
 
3.9%
369
 
3.2%
866
 
3.0%
Other values (4)240
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e590
 
8.5%
-534
 
7.7%
s449
 
6.5%
/425
 
6.1%
t420
 
6.1%
o365
 
5.3%
a299
 
4.3%
w276
 
4.0%
m255
 
3.7%
i255
 
3.7%
Other values (30)3048
44.1%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct79
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size808.0 B
Episode 9
 
3
Episode 10
 
2
Episode 28
 
2
Episode 27
 
2
Episode 8
 
2
Other values (74)
74 

Length

Max length93
Median length62
Mean length20.15294118
Min length1

Characters and Unicode

Total characters1713
Distinct characters128
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)87.1%

Sample

1st rowКОНТАКТЫ в телефоне Николая Соболева: Клава Кока, Эльдар Джарахов, Андрей Малахов, Эдвард Бил
2nd rowСерия 8
3rd rowСерия 1
4th rowСерия 2
5th rowEpisode 9

Common Values

ValueCountFrequency (%)
Episode 93
 
3.5%
Episode 102
 
2.4%
Episode 282
 
2.4%
Episode 272
 
2.4%
Episode 82
 
2.4%
Heart & Soul1
 
1.2%
Episode 71
 
1.2%
Episode 181
 
1.2%
Episode 171
 
1.2%
Episode 241
 
1.2%
Other values (69)69
81.2%

Length

2022-05-09T21:05:25.344390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode29
 
9.9%
10
 
3.4%
95
 
1.7%
the5
 
1.7%
24
 
1.4%
to4
 
1.4%
серия3
 
1.0%
13
 
1.0%
83
 
1.0%
vs3
 
1.0%
Other values (211)225
76.5%

Most occurring characters

ValueCountFrequency (%)
210
 
12.3%
e128
 
7.5%
o93
 
5.4%
i80
 
4.7%
a74
 
4.3%
r69
 
4.0%
s68
 
4.0%
n61
 
3.6%
t55
 
3.2%
d49
 
2.9%
Other values (118)826
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1141
66.6%
Uppercase Letter242
 
14.1%
Space Separator210
 
12.3%
Decimal Number74
 
4.3%
Other Punctuation35
 
2.0%
Dash Punctuation8
 
0.5%
Close Punctuation1
 
0.1%
Math Symbol1
 
0.1%
Open Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e128
 
11.2%
o93
 
8.2%
i80
 
7.0%
a74
 
6.5%
r69
 
6.0%
s68
 
6.0%
n61
 
5.3%
t55
 
4.8%
d49
 
4.3%
p44
 
3.9%
Other values (49)420
36.8%
Uppercase Letter
ValueCountFrequency (%)
E35
 
14.5%
B13
 
5.4%
T13
 
5.4%
H13
 
5.4%
S12
 
5.0%
D10
 
4.1%
L10
 
4.1%
F9
 
3.7%
A9
 
3.7%
M9
 
3.7%
Other values (35)109
45.0%
Decimal Number
ValueCountFrequency (%)
217
23.0%
115
20.3%
010
13.5%
87
9.5%
96
 
8.1%
35
 
6.8%
75
 
6.8%
44
 
5.4%
53
 
4.1%
62
 
2.7%
Other Punctuation
ValueCountFrequency (%)
,11
31.4%
.7
20.0%
&5
14.3%
:4
 
11.4%
!3
 
8.6%
"2
 
5.7%
'1
 
2.9%
/1
 
2.9%
?1
 
2.9%
Space Separator
ValueCountFrequency (%)
210
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Math Symbol
ValueCountFrequency (%)
|1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1107
64.6%
Common330
 
19.3%
Cyrillic276
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e128
 
11.6%
o93
 
8.4%
i80
 
7.2%
a74
 
6.7%
r69
 
6.2%
s68
 
6.1%
n61
 
5.5%
t55
 
5.0%
d49
 
4.4%
p44
 
4.0%
Other values (42)386
34.9%
Cyrillic
ValueCountFrequency (%)
а25
 
9.1%
е24
 
8.7%
о16
 
5.8%
и15
 
5.4%
р14
 
5.1%
н13
 
4.7%
л12
 
4.3%
к11
 
4.0%
в9
 
3.3%
К8
 
2.9%
Other values (42)129
46.7%
Common
ValueCountFrequency (%)
210
63.6%
217
 
5.2%
115
 
4.5%
,11
 
3.3%
010
 
3.0%
-8
 
2.4%
.7
 
2.1%
87
 
2.1%
96
 
1.8%
&5
 
1.5%
Other values (14)34
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1430
83.5%
Cyrillic276
 
16.1%
None7
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
 
14.7%
e128
 
9.0%
o93
 
6.5%
i80
 
5.6%
a74
 
5.2%
r69
 
4.8%
s68
 
4.8%
n61
 
4.3%
t55
 
3.8%
d49
 
3.4%
Other values (62)543
38.0%
Cyrillic
ValueCountFrequency (%)
а25
 
9.1%
е24
 
8.7%
о16
 
5.8%
и15
 
5.4%
р14
 
5.1%
н13
 
4.7%
л12
 
4.3%
к11
 
4.0%
в9
 
3.3%
К8
 
2.9%
Other values (42)129
46.7%
None
ValueCountFrequency (%)
ó2
28.6%
í2
28.6%
á2
28.6%
ø1
14.3%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.71764706
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:25.474393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile28.4
Maximum2020
Range2019
Interquartile range (IQR)2

Descriptive statistics

Standard deviation429.7304973
Coefficient of variation (CV)4.397675448
Kurtosis17.37041999
Mean97.71764706
Median Absolute Deviation (MAD)0
Skewness4.354294866
Sum8306
Variance184668.3003
MonotonicityNot monotonic
2022-05-09T21:05:25.664177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
151
60.0%
212
 
14.1%
20204
 
4.7%
44
 
4.7%
73
 
3.5%
32
 
2.4%
52
 
2.4%
101
 
1.2%
81
 
1.2%
61
 
1.2%
Other values (4)4
 
4.7%
ValueCountFrequency (%)
151
60.0%
212
 
14.1%
32
 
2.4%
44
 
4.7%
52
 
2.4%
61
 
1.2%
73
 
3.5%
81
 
1.2%
101
 
1.2%
111
 
1.2%
ValueCountFrequency (%)
20204
4.7%
311
 
1.2%
181
 
1.2%
141
 
1.2%
111
 
1.2%
101
 
1.2%
81
 
1.2%
73
3.5%
61
 
1.2%
52
2.4%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)48.1%
Missing4
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean27.91358025
Minimum1
Maximum296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:25.839179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q328
95-th percentile98
Maximum296
Range295
Interquartile range (IQR)24

Descriptive statistics

Standard deviation50.35950693
Coefficient of variation (CV)1.804122097
Kurtosis19.00034931
Mean27.91358025
Median Absolute Deviation (MAD)8
Skewness4.08703418
Sum2261
Variance2536.079938
MonotonicityNot monotonic
2022-05-09T21:05:26.044580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
28
 
9.4%
15
 
5.9%
35
 
5.9%
85
 
5.9%
94
 
4.7%
104
 
4.7%
73
 
3.5%
53
 
3.5%
183
 
3.5%
43
 
3.5%
Other values (29)38
44.7%
(Missing)4
 
4.7%
ValueCountFrequency (%)
15
5.9%
28
9.4%
35
5.9%
43
 
3.5%
53
 
3.5%
61
 
1.2%
73
 
3.5%
85
5.9%
94
4.7%
104
4.7%
ValueCountFrequency (%)
2961
1.2%
2951
1.2%
1481
1.2%
1101
1.2%
981
1.2%
801
1.2%
621
1.2%
611
1.2%
591
1.2%
561
1.2%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
regular
81 
significant_special
 
3
insignificant_special
 
1

Length

Max length21
Median length7
Mean length7.588235294
Min length7

Characters and Unicode

Total characters645
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular81
95.3%
significant_special3
 
3.5%
insignificant_special1
 
1.2%

Length

2022-05-09T21:05:26.189301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:05:26.404303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular81
95.3%
significant_special3
 
3.5%
insignificant_special1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r162
25.1%
a89
13.8%
e85
13.2%
g85
13.2%
l85
13.2%
u81
12.6%
i17
 
2.6%
n9
 
1.4%
s8
 
1.2%
c8
 
1.2%
Other values (4)16
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter641
99.4%
Connector Punctuation4
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r162
25.3%
a89
13.9%
e85
13.3%
g85
13.3%
l85
13.3%
u81
12.6%
i17
 
2.7%
n9
 
1.4%
s8
 
1.2%
c8
 
1.2%
Other values (3)12
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin641
99.4%
Common4
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r162
25.3%
a89
13.9%
e85
13.3%
g85
13.3%
l85
13.3%
u81
12.6%
i17
 
2.7%
n9
 
1.4%
s8
 
1.2%
c8
 
1.2%
Other values (3)12
 
1.9%
Common
ValueCountFrequency (%)
_4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r162
25.1%
a89
13.8%
e85
13.2%
g85
13.2%
l85
13.2%
u81
12.6%
i17
 
2.6%
n9
 
1.4%
s8
 
1.2%
c8
 
1.2%
Other values (4)16
 
2.5%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-09
85 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-09
2nd row2020-12-09
3rd row2020-12-09
4th row2020-12-09
5th row2020-12-09

Common Values

ValueCountFrequency (%)
2020-12-0985
100.0%

Length

2022-05-09T21:05:26.535902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:05:26.704213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0985
100.0%

Most occurring characters

ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
985
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number680
80.0%
Dash Punctuation170
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2255
37.5%
0255
37.5%
185
 
12.5%
985
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
985
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2255
30.0%
0255
30.0%
-170
20.0%
185
 
10.0%
985
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
47 
20:00
15 
06:00
19:30
 
4
10:00
 
3
Other values (8)
10 

Length

Max length5
Median length3
Mean length3.894117647
Min length3

Characters and Unicode

Total characters331
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st row12:00
2nd rownan
3rd rownan
4th rownan
5th row11:00

Common Values

ValueCountFrequency (%)
nan47
55.3%
20:0015
 
17.6%
06:006
 
7.1%
19:304
 
4.7%
10:003
 
3.5%
12:002
 
2.4%
00:002
 
2.4%
11:001
 
1.2%
05:001
 
1.2%
17:351
 
1.2%
Other values (3)3
 
3.5%

Length

2022-05-09T21:05:26.824199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan47
55.3%
20:0015
 
17.6%
06:006
 
7.1%
19:304
 
4.7%
10:003
 
3.5%
12:002
 
2.4%
00:002
 
2.4%
11:001
 
1.2%
05:001
 
1.2%
17:351
 
1.2%
Other values (3)3
 
3.5%

Most occurring characters

ValueCountFrequency (%)
097
29.3%
n94
28.4%
a47
14.2%
:38
 
11.5%
220
 
6.0%
114
 
4.2%
66
 
1.8%
36
 
1.8%
95
 
1.5%
53
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number152
45.9%
Lowercase Letter141
42.6%
Other Punctuation38
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
097
63.8%
220
 
13.2%
114
 
9.2%
66
 
3.9%
36
 
3.9%
95
 
3.3%
53
 
2.0%
71
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
n94
66.7%
a47
33.3%
Other Punctuation
ValueCountFrequency (%)
:38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common190
57.4%
Latin141
42.6%

Most frequent character per script

Common
ValueCountFrequency (%)
097
51.1%
:38
 
20.0%
220
 
10.5%
114
 
7.4%
66
 
3.2%
36
 
3.2%
95
 
2.6%
53
 
1.6%
71
 
0.5%
Latin
ValueCountFrequency (%)
n94
66.7%
a47
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
097
29.3%
n94
28.4%
a47
14.2%
:38
 
11.5%
220
 
6.0%
114
 
4.2%
66
 
1.8%
36
 
1.8%
95
 
1.5%
53
 
0.9%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-09T12:00:00+00:00
42 
2020-12-09T05:00:00+00:00
2020-12-09T11:00:00+00:00
2020-12-09T04:00:00+00:00
2020-12-09T19:30:00+00:00
 
4
Other values (14)
23 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2125
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)10.6%

Sample

1st row2020-12-09T00:00:00+00:00
2nd row2020-12-09T00:00:00+00:00
3rd row2020-12-09T00:00:00+00:00
4th row2020-12-09T00:00:00+00:00
5th row2020-12-09T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-09T12:00:00+00:0042
49.4%
2020-12-09T05:00:00+00:006
 
7.1%
2020-12-09T11:00:00+00:005
 
5.9%
2020-12-09T04:00:00+00:005
 
5.9%
2020-12-09T19:30:00+00:004
 
4.7%
2020-12-09T00:00:00+00:004
 
4.7%
2020-12-09T02:00:00+00:003
 
3.5%
2020-12-09T17:00:00+00:003
 
3.5%
2020-12-09T10:00:00+00:002
 
2.4%
2020-12-09T15:00:00+00:002
 
2.4%
Other values (9)9
 
10.6%

Length

2022-05-09T21:05:26.954140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-09t12:00:00+00:0042
49.4%
2020-12-09t05:00:00+00:006
 
7.1%
2020-12-09t11:00:00+00:005
 
5.9%
2020-12-09t04:00:00+00:005
 
5.9%
2020-12-09t19:30:00+00:004
 
4.7%
2020-12-09t00:00:00+00:004
 
4.7%
2020-12-09t02:00:00+00:003
 
3.5%
2020-12-09t17:00:00+00:003
 
3.5%
2020-12-09t15:00:00+00:002
 
2.4%
2020-12-09t10:00:00+00:002
 
2.4%
Other values (9)9
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0956
45.0%
2302
 
14.2%
:255
 
12.0%
-170
 
8.0%
1155
 
7.3%
987
 
4.1%
T85
 
4.0%
+85
 
4.0%
512
 
0.6%
37
 
0.3%
Other values (3)11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1530
72.0%
Other Punctuation255
 
12.0%
Dash Punctuation170
 
8.0%
Uppercase Letter85
 
4.0%
Math Symbol85
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0956
62.5%
2302
 
19.7%
1155
 
10.1%
987
 
5.7%
512
 
0.8%
37
 
0.5%
46
 
0.4%
74
 
0.3%
81
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:255
100.0%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%
Uppercase Letter
ValueCountFrequency (%)
T85
100.0%
Math Symbol
ValueCountFrequency (%)
+85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2040
96.0%
Latin85
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0956
46.9%
2302
 
14.8%
:255
 
12.5%
-170
 
8.3%
1155
 
7.6%
987
 
4.3%
+85
 
4.2%
512
 
0.6%
37
 
0.3%
46
 
0.3%
Other values (2)5
 
0.2%
Latin
ValueCountFrequency (%)
T85
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0956
45.0%
2302
 
14.2%
:255
 
12.0%
-170
 
8.0%
1155
 
7.3%
987
 
4.1%
T85
 
4.0%
+85
 
4.0%
512
 
0.6%
37
 
0.3%
Other values (3)11
 
0.5%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)44.4%
Missing4
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean37.54320988
Minimum2
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:27.085166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q119
median30
Q345
95-th percentile106
Maximum136
Range134
Interquartile range (IQR)26

Descriptive statistics

Standard deviation27.75024747
Coefficient of variation (CV)0.739154898
Kurtosis3.271233397
Mean37.54320988
Median Absolute Deviation (MAD)15
Skewness1.648604528
Sum3041
Variance770.0762346
MonotonicityNot monotonic
2022-05-09T21:05:27.311004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4516
18.8%
309
 
10.6%
255
 
5.9%
604
 
4.7%
333
 
3.5%
53
 
3.5%
73
 
3.5%
123
 
3.5%
433
 
3.5%
132
 
2.4%
Other values (26)30
35.3%
(Missing)4
 
4.7%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
53
3.5%
73
3.5%
81
 
1.2%
101
 
1.2%
123
3.5%
132
2.4%
152
2.4%
161
 
1.2%
ValueCountFrequency (%)
1361
 
1.2%
1211
 
1.2%
1202
2.4%
1061
 
1.2%
901
 
1.2%
721
 
1.2%
604
4.7%
581
 
1.2%
551
 
1.2%
531
 
1.2%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
66 
<p>Go ahead. Ask her why she does it. Trixie and Katya spill the tea on power, power dynamics, and how to use your boobs to wield it over others,</p>
 
1
<p>Happy Holidays! BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World), Ruth Righi (Sydney to the Max), and special guest Izabela Rose (Upside-Down Magic) compete to see who can make the best gingerbread house! </p>
 
1
<p>On the last leg of his journey across England, Robbie crosses an epic aqueduct near Stratford-upon-Avon and gets stuck in a lock in central Birmingham.</p>
 
1
<p>Robbie navigates the mighty River Severn and takes an unexpected bath as he takes a tumble at the Tardebigge lock flight in Worcestershire.</p>
 
1
Other values (15)
15 

Length

Max length514
Median length3
Mean length43.51764706
Min length3

Characters and Unicode

Total characters3699
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)22.4%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan66
77.6%
<p>Go ahead. Ask her why she does it. Trixie and Katya spill the tea on power, power dynamics, and how to use your boobs to wield it over others,</p>1
 
1.2%
<p>Happy Holidays! BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World), Ruth Righi (Sydney to the Max), and special guest Izabela Rose (Upside-Down Magic) compete to see who can make the best gingerbread house! </p>1
 
1.2%
<p>On the last leg of his journey across England, Robbie crosses an epic aqueduct near Stratford-upon-Avon and gets stuck in a lock in central Birmingham.</p>1
 
1.2%
<p>Robbie navigates the mighty River Severn and takes an unexpected bath as he takes a tumble at the Tardebigge lock flight in Worcestershire.</p>1
 
1.2%
<p>Robbie gets stuck in the mud in Woodseaves Cutting and explores the charming canal-side village of Kinver in Staffordshire.</p>1
 
1.2%
<p>Robbie battles his way through blanket weed on the Shropshire Union Canal and discovers industrial secrets in Audlem, Cheshire.</p>1
 
1.2%
<p>Hot Take breaks down the latest news about the Covid vaccine, the Georgia recount, and more with actor and activist Kal Penn. Plus, Tyler Templeton asks Uncle Squirrel to donate his organs to Rudy Giuliani. </p>1
 
1.2%
<p>Welcome to the SEASON 2 of our spooky and now FESTIVE show- Too Many Spirits! Join us as we read your submitted holiday ghost stories and enjoy cocktails prepared by freshman bartender, Steven Lim.</p>1
 
1.2%
<p>Arm takes viewers backstage of the MAMA OK photoshoot.</p>1
 
1.2%
Other values (10)10
 
11.8%

Length

2022-05-09T21:05:27.496790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan66
 
10.3%
the31
 
4.8%
and22
 
3.4%
to18
 
2.8%
a15
 
2.3%
in10
 
1.6%
of10
 
1.6%
with7
 
1.1%
his7
 
1.1%
p7
 
1.1%
Other values (361)449
69.9%

Most occurring characters

ValueCountFrequency (%)
550
14.9%
n304
 
8.2%
e297
 
8.0%
a290
 
7.8%
t212
 
5.7%
o202
 
5.5%
i186
 
5.0%
s171
 
4.6%
r160
 
4.3%
h129
 
3.5%
Other values (62)1198
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2748
74.3%
Space Separator559
 
15.1%
Uppercase Letter168
 
4.5%
Other Punctuation107
 
2.9%
Math Symbol96
 
2.6%
Dash Punctuation12
 
0.3%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Decimal Number3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n304
11.1%
e297
10.8%
a290
10.6%
t212
 
7.7%
o202
 
7.4%
i186
 
6.8%
s171
 
6.2%
r160
 
5.8%
h129
 
4.7%
p104
 
3.8%
Other values (18)693
25.2%
Uppercase Letter
ValueCountFrequency (%)
S22
13.1%
T18
 
10.7%
A17
 
10.1%
R15
 
8.9%
M12
 
7.1%
C10
 
6.0%
K7
 
4.2%
L6
 
3.6%
P6
 
3.6%
D6
 
3.6%
Other values (15)49
29.2%
Other Punctuation
ValueCountFrequency (%)
.36
33.6%
,35
32.7%
/26
24.3%
!3
 
2.8%
"2
 
1.9%
'2
 
1.9%
:1
 
0.9%
&1
 
0.9%
;1
 
0.9%
Space Separator
ValueCountFrequency (%)
550
98.4%
 9
 
1.6%
Math Symbol
ValueCountFrequency (%)
>48
50.0%
<48
50.0%
Dash Punctuation
ValueCountFrequency (%)
-10
83.3%
2
 
16.7%
Decimal Number
ValueCountFrequency (%)
22
66.7%
11
33.3%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2916
78.8%
Common783
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n304
 
10.4%
e297
 
10.2%
a290
 
9.9%
t212
 
7.3%
o202
 
6.9%
i186
 
6.4%
s171
 
5.9%
r160
 
5.5%
h129
 
4.4%
p104
 
3.6%
Other values (43)861
29.5%
Common
ValueCountFrequency (%)
550
70.2%
>48
 
6.1%
<48
 
6.1%
.36
 
4.6%
,35
 
4.5%
/26
 
3.3%
-10
 
1.3%
 9
 
1.1%
(3
 
0.4%
)3
 
0.4%
Other values (9)15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3686
99.6%
None11
 
0.3%
Punctuation2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
550
14.9%
n304
 
8.2%
e297
 
8.1%
a290
 
7.9%
t212
 
5.8%
o202
 
5.5%
i186
 
5.0%
s171
 
4.6%
r160
 
4.3%
h129
 
3.5%
Other values (58)1185
32.1%
None
ValueCountFrequency (%)
 9
81.8%
ñ1
 
9.1%
í1
 
9.1%
Punctuation
ValueCountFrequency (%)
2
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46883.14118
Minimum2266
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:27.700385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2266
5-th percentile16778.6
Q145155
median51992
Q352639
95-th percentile57900.2
Maximum61755
Range59489
Interquartile range (IQR)7484

Descriptive statistics

Standard deviation11975.32217
Coefficient of variation (CV)0.255429177
Kurtosis4.513759917
Mean46883.14118
Median Absolute Deviation (MAD)3927
Skewness-2.116721595
Sum3985067
Variance143408341
MonotonicityNot monotonic
2022-05-09T21:05:28.172346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
526395
 
5.9%
451554
 
4.7%
519924
 
4.7%
586892
 
2.4%
521082
 
2.4%
152502
 
2.4%
524212
 
2.4%
524002
 
2.4%
521592
 
2.4%
570302
 
2.4%
Other values (55)58
68.2%
ValueCountFrequency (%)
22661
1.2%
25041
1.2%
129061
1.2%
152502
2.4%
228931
1.2%
262681
1.2%
270551
1.2%
283461
1.2%
339441
1.2%
340601
1.2%
ValueCountFrequency (%)
617551
1.2%
586892
2.4%
583671
1.2%
579531
1.2%
576891
1.2%
574781
1.2%
570302
2.4%
567461
1.2%
565311
1.2%
559191
1.2%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct65
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://www.tvmaze.com/shows/52639/puckers
 
5
https://www.tvmaze.com/shows/45155/canal-boat-diaries
 
4
https://www.tvmaze.com/shows/51992/the-surgeons-cut
 
4
https://www.tvmaze.com/shows/58689/my-supernatural-power
 
2
https://www.tvmaze.com/shows/52108/psych-hunter
 
2
Other values (60)
68 

Length

Max length74
Median length61
Mean length50.14117647
Min length41

Characters and Unicode

Total characters4262
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)61.2%

Sample

1st rowhttps://www.tvmaze.com/shows/49630/kontakty
2nd rowhttps://www.tvmaze.com/shows/52198/kotiki
3rd rowhttps://www.tvmaze.com/shows/52316/mertvye-dusi
4th rowhttps://www.tvmaze.com/shows/52316/mertvye-dusi
5th rowhttps://www.tvmaze.com/shows/47207/mermaid-prince

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52639/puckers5
 
5.9%
https://www.tvmaze.com/shows/45155/canal-boat-diaries4
 
4.7%
https://www.tvmaze.com/shows/51992/the-surgeons-cut4
 
4.7%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
2.4%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.4%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.4%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
2.4%
https://www.tvmaze.com/shows/52400/dream-detective2
 
2.4%
https://www.tvmaze.com/shows/52159/to-love2
 
2.4%
https://www.tvmaze.com/shows/57030/gjor-det-sjol2
 
2.4%
Other values (55)58
68.2%

Length

2022-05-09T21:05:28.455446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52639/puckers5
 
5.9%
https://www.tvmaze.com/shows/51992/the-surgeons-cut4
 
4.7%
https://www.tvmaze.com/shows/45155/canal-boat-diaries4
 
4.7%
https://www.tvmaze.com/shows/52159/to-love2
 
2.4%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.4%
https://www.tvmaze.com/shows/52107/new-face2
 
2.4%
https://www.tvmaze.com/shows/57030/gjor-det-sjol2
 
2.4%
https://www.tvmaze.com/shows/52316/mertvye-dusi2
 
2.4%
https://www.tvmaze.com/shows/52400/dream-detective2
 
2.4%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
2.4%
Other values (55)58
68.2%

Most occurring characters

ValueCountFrequency (%)
/425
 
10.0%
w354
 
8.3%
t342
 
8.0%
s341
 
8.0%
o247
 
5.8%
e224
 
5.3%
h214
 
5.0%
m210
 
4.9%
a174
 
4.1%
.170
 
4.0%
Other values (29)1561
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3004
70.5%
Other Punctuation680
 
16.0%
Decimal Number430
 
10.1%
Dash Punctuation148
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w354
11.8%
t342
11.4%
s341
11.4%
o247
 
8.2%
e224
 
7.5%
h214
 
7.1%
m210
 
7.0%
a174
 
5.8%
c123
 
4.1%
p109
 
3.6%
Other values (15)666
22.2%
Decimal Number
ValueCountFrequency (%)
586
20.0%
251
11.9%
450
11.6%
142
9.8%
038
8.8%
938
8.8%
336
8.4%
633
 
7.7%
731
 
7.2%
825
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/425
62.5%
.170
 
25.0%
:85
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3004
70.5%
Common1258
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w354
11.8%
t342
11.4%
s341
11.4%
o247
 
8.2%
e224
 
7.5%
h214
 
7.1%
m210
 
7.0%
a174
 
5.8%
c123
 
4.1%
p109
 
3.6%
Other values (15)666
22.2%
Common
ValueCountFrequency (%)
/425
33.8%
.170
 
13.5%
-148
 
11.8%
586
 
6.8%
:85
 
6.8%
251
 
4.1%
450
 
4.0%
142
 
3.3%
038
 
3.0%
938
 
3.0%
Other values (4)125
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/425
 
10.0%
w354
 
8.3%
t342
 
8.0%
s341
 
8.0%
o247
 
5.8%
e224
 
5.3%
h214
 
5.0%
m210
 
4.9%
a174
 
4.1%
.170
 
4.0%
Other values (29)1561
36.6%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct65
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
Puckers
 
5
Canal Boat Diaries
 
4
The Surgeon's Cut
 
4
My Supernatural Power
 
2
Psych Hunter
 
2
Other values (60)
68 

Length

Max length40
Median length26
Mean length15.42352941
Min length6

Characters and Unicode

Total characters1311
Distinct characters99
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)61.2%

Sample

1st rowКонтакты
2nd rowКотики
3rd rowМёртвые души
4th rowМёртвые души
5th rowMermaid Prince

Common Values

ValueCountFrequency (%)
Puckers5
 
5.9%
Canal Boat Diaries4
 
4.7%
The Surgeon's Cut4
 
4.7%
My Supernatural Power2
 
2.4%
Psych Hunter2
 
2.4%
The Young Turks2
 
2.4%
You Complete Me2
 
2.4%
Dream Detective2
 
2.4%
To Love2
 
2.4%
Gjør det sjøl2
 
2.4%
Other values (55)58
68.2%

Length

2022-05-09T21:05:28.691639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the13
 
5.6%
puckers5
 
2.1%
boat4
 
1.7%
diaries4
 
1.7%
surgeon's4
 
1.7%
cut4
 
1.7%
love4
 
1.7%
canal4
 
1.7%
of4
 
1.7%
you3
 
1.3%
Other values (158)185
79.1%

Most occurring characters

ValueCountFrequency (%)
149
 
11.4%
e124
 
9.5%
a73
 
5.6%
r60
 
4.6%
n59
 
4.5%
o58
 
4.4%
s55
 
4.2%
i55
 
4.2%
t53
 
4.0%
u47
 
3.6%
Other values (89)578
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter927
70.7%
Uppercase Letter208
 
15.9%
Space Separator149
 
11.4%
Other Punctuation17
 
1.3%
Decimal Number7
 
0.5%
Currency Symbol2
 
0.2%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e124
13.4%
a73
 
7.9%
r60
 
6.5%
n59
 
6.4%
o58
 
6.3%
s55
 
5.9%
i55
 
5.9%
t53
 
5.7%
u47
 
5.1%
h37
 
4.0%
Other values (42)306
33.0%
Uppercase Letter
ValueCountFrequency (%)
T24
 
11.5%
S22
 
10.6%
C18
 
8.7%
M15
 
7.2%
P12
 
5.8%
D12
 
5.8%
B12
 
5.8%
Y10
 
4.8%
N8
 
3.8%
F8
 
3.8%
Other values (21)67
32.2%
Other Punctuation
ValueCountFrequency (%)
'7
41.2%
:4
23.5%
.1
 
5.9%
&1
 
5.9%
,1
 
5.9%
?1
 
5.9%
@1
 
5.9%
#1
 
5.9%
Decimal Number
ValueCountFrequency (%)
03
42.9%
22
28.6%
11
 
14.3%
51
 
14.3%
Currency Symbol
ValueCountFrequency (%)
$1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
149
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1014
77.3%
Common176
 
13.4%
Cyrillic121
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e124
 
12.2%
a73
 
7.2%
r60
 
5.9%
n59
 
5.8%
o58
 
5.7%
s55
 
5.4%
i55
 
5.4%
t53
 
5.2%
u47
 
4.6%
h37
 
3.6%
Other values (39)393
38.8%
Cyrillic
ValueCountFrequency (%)
о10
 
8.3%
т10
 
8.3%
р9
 
7.4%
и8
 
6.6%
е8
 
6.6%
а8
 
6.6%
к8
 
6.6%
у5
 
4.1%
з4
 
3.3%
м4
 
3.3%
Other values (24)47
38.8%
Common
ValueCountFrequency (%)
149
84.7%
'7
 
4.0%
:4
 
2.3%
03
 
1.7%
22
 
1.1%
11
 
0.6%
.1
 
0.6%
51
 
0.6%
&1
 
0.6%
,1
 
0.6%
Other values (6)6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1182
90.2%
Cyrillic121
 
9.2%
None7
 
0.5%
Currency Symbols1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
149
 
12.6%
e124
 
10.5%
a73
 
6.2%
r60
 
5.1%
n59
 
5.0%
o58
 
4.9%
s55
 
4.7%
i55
 
4.7%
t53
 
4.5%
u47
 
4.0%
Other values (53)449
38.0%
Cyrillic
ValueCountFrequency (%)
о10
 
8.3%
т10
 
8.3%
р9
 
7.4%
и8
 
6.6%
е8
 
6.6%
а8
 
6.6%
к8
 
6.6%
у5
 
4.1%
з4
 
3.3%
м4
 
3.3%
Other values (24)47
38.8%
None
ValueCountFrequency (%)
ø7
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
Scripted
32 
Documentary
13 
Talk Show
11 
Animation
10 
Reality
Other values (4)
10 

Length

Max length11
Median length9
Mean length8.458823529
Min length4

Characters and Unicode

Total characters719
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowGame Show
2nd rowScripted
3rd rowScripted
4th rowScripted
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted32
37.6%
Documentary13
15.3%
Talk Show11
 
12.9%
Animation10
 
11.8%
Reality9
 
10.6%
Game Show4
 
4.7%
News3
 
3.5%
Variety2
 
2.4%
Sports1
 
1.2%

Length

2022-05-09T21:05:28.876287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:05:29.220498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted32
32.0%
show15
15.0%
documentary13
13.0%
talk11
 
11.0%
animation10
 
10.0%
reality9
 
9.0%
game4
 
4.0%
news3
 
3.0%
variety2
 
2.0%
sports1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t67
 
9.3%
e63
 
8.8%
i63
 
8.8%
a49
 
6.8%
S48
 
6.7%
r48
 
6.7%
c45
 
6.3%
o39
 
5.4%
p33
 
4.6%
n33
 
4.6%
Other values (17)231
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter604
84.0%
Uppercase Letter100
 
13.9%
Space Separator15
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t67
11.1%
e63
10.4%
i63
10.4%
a49
 
8.1%
r48
 
7.9%
c45
 
7.5%
o39
 
6.5%
p33
 
5.5%
n33
 
5.5%
d32
 
5.3%
Other values (8)132
21.9%
Uppercase Letter
ValueCountFrequency (%)
S48
48.0%
D13
 
13.0%
T11
 
11.0%
A10
 
10.0%
R9
 
9.0%
G4
 
4.0%
N3
 
3.0%
V2
 
2.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin704
97.9%
Common15
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t67
 
9.5%
e63
 
8.9%
i63
 
8.9%
a49
 
7.0%
S48
 
6.8%
r48
 
6.8%
c45
 
6.4%
o39
 
5.5%
p33
 
4.7%
n33
 
4.7%
Other values (16)216
30.7%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t67
 
9.3%
e63
 
8.8%
i63
 
8.8%
a49
 
6.8%
S48
 
6.7%
r48
 
6.7%
c45
 
6.3%
o39
 
5.4%
p33
 
4.6%
n33
 
4.6%
Other values (17)231
32.1%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
English
23 
Chinese
19 
Norwegian
13 
Russian
Thai
Other values (12)
18 

Length

Max length10
Median length7
Mean length7.070588235
Min length3

Characters and Unicode

Total characters601
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)9.4%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowKorean

Common Values

ValueCountFrequency (%)
English23
27.1%
Chinese19
22.4%
Norwegian13
15.3%
Russian8
 
9.4%
Thai4
 
4.7%
Arabic3
 
3.5%
Korean3
 
3.5%
nan2
 
2.4%
Ukrainian2
 
2.4%
Dutch1
 
1.2%
Other values (7)7
 
8.2%

Length

2022-05-09T21:05:29.575850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english23
27.1%
chinese19
22.4%
norwegian13
15.3%
russian8
 
9.4%
thai4
 
4.7%
arabic3
 
3.5%
korean3
 
3.5%
ukrainian2
 
2.4%
nan2
 
2.4%
dutch1
 
1.2%
Other values (7)7
 
8.2%

Most occurring characters

ValueCountFrequency (%)
n79
13.1%
i77
12.8%
s62
10.3%
e60
10.0%
h51
8.5%
a44
 
7.3%
g38
 
6.3%
r24
 
4.0%
E23
 
3.8%
l23
 
3.8%
Other values (23)120
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter518
86.2%
Uppercase Letter83
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n79
15.3%
i77
14.9%
s62
12.0%
e60
11.6%
h51
9.8%
a44
8.5%
g38
7.3%
r24
 
4.6%
l23
 
4.4%
o17
 
3.3%
Other values (9)43
8.3%
Uppercase Letter
ValueCountFrequency (%)
E23
27.7%
C19
22.9%
N13
15.7%
R8
 
9.6%
K4
 
4.8%
T4
 
4.8%
A3
 
3.6%
U2
 
2.4%
S2
 
2.4%
D1
 
1.2%
Other values (4)4
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n79
13.1%
i77
12.8%
s62
10.3%
e60
10.0%
h51
8.5%
a44
 
7.3%
g38
 
6.3%
r24
 
4.0%
E23
 
3.8%
l23
 
3.8%
Other values (23)120
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n79
13.1%
i77
12.8%
s62
10.3%
e60
10.0%
h51
8.5%
a44
 
7.3%
g38
 
6.3%
r24
 
4.0%
E23
 
3.8%
l23
 
3.8%
Other values (23)120
20.0%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
[]
17 
['Comedy']
13 
['Drama', 'Romance']
['Sports']
['Travel']
Other values (26)
37 

Length

Max length40
Median length35
Mean length15.62352941
Min length2

Characters and Unicode

Total characters1328
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)20.0%

Sample

1st row[]
2nd row['Comedy']
3rd row['Comedy']
4th row['Comedy']
5th row['Drama', 'Romance', 'Mystery']

Common Values

ValueCountFrequency (%)
[]17
20.0%
['Comedy']13
15.3%
['Drama', 'Romance']7
 
8.2%
['Sports']6
 
7.1%
['Travel']5
 
5.9%
['Medical']4
 
4.7%
['Music']2
 
2.4%
['Drama', 'Fantasy', 'Mystery']2
 
2.4%
['Crime', 'Thriller', 'Mystery']2
 
2.4%
['Drama', 'Thriller', 'Mystery']2
 
2.4%
Other values (21)25
29.4%

Length

2022-05-09T21:05:29.798486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy26
18.3%
drama20
14.1%
17
12.0%
romance12
 
8.5%
mystery7
 
4.9%
children6
 
4.2%
thriller6
 
4.2%
sports6
 
4.2%
fantasy5
 
3.5%
travel5
 
3.5%
Other values (12)32
22.5%

Most occurring characters

ValueCountFrequency (%)
'250
18.8%
[85
 
6.4%
]85
 
6.4%
e81
 
6.1%
a79
 
5.9%
m71
 
5.3%
r70
 
5.3%
,57
 
4.3%
57
 
4.3%
o54
 
4.1%
Other values (24)439
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter665
50.1%
Other Punctuation307
23.1%
Uppercase Letter128
 
9.6%
Open Punctuation85
 
6.4%
Close Punctuation85
 
6.4%
Space Separator57
 
4.3%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e81
12.2%
a79
11.9%
m71
10.7%
r70
10.5%
o54
8.1%
y51
7.7%
i41
 
6.2%
n38
 
5.7%
d37
 
5.6%
l33
 
5.0%
Other values (7)110
16.5%
Uppercase Letter
ValueCountFrequency (%)
C36
28.1%
D21
16.4%
M13
 
10.2%
R12
 
9.4%
A11
 
8.6%
T11
 
8.6%
F10
 
7.8%
S9
 
7.0%
H3
 
2.3%
I1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
'250
81.4%
,57
 
18.6%
Open Punctuation
ValueCountFrequency (%)
[85
100.0%
Close Punctuation
ValueCountFrequency (%)
]85
100.0%
Space Separator
ValueCountFrequency (%)
57
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin793
59.7%
Common535
40.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e81
 
10.2%
a79
 
10.0%
m71
 
9.0%
r70
 
8.8%
o54
 
6.8%
y51
 
6.4%
i41
 
5.2%
n38
 
4.8%
d37
 
4.7%
C36
 
4.5%
Other values (18)235
29.6%
Common
ValueCountFrequency (%)
'250
46.7%
[85
 
15.9%
]85
 
15.9%
,57
 
10.7%
57
 
10.7%
-1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'250
18.8%
[85
 
6.4%
]85
 
6.4%
e81
 
6.1%
a79
 
5.9%
m71
 
5.3%
r70
 
5.3%
,57
 
4.3%
57
 
4.3%
o54
 
4.1%
Other values (24)439
33.1%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
Running
45 
Ended
33 
To Be Determined

Length

Max length16
Median length7
Mean length6.964705882
Min length5

Characters and Unicode

Total characters592
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowEnded
4th rowEnded
5th rowEnded

Common Values

ValueCountFrequency (%)
Running45
52.9%
Ended33
38.8%
To Be Determined7
 
8.2%

Length

2022-05-09T21:05:30.096219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:05:30.356282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running45
45.5%
ended33
33.3%
to7
 
7.1%
be7
 
7.1%
determined7
 
7.1%

Most occurring characters

ValueCountFrequency (%)
n175
29.6%
d73
12.3%
e61
 
10.3%
i52
 
8.8%
R45
 
7.6%
u45
 
7.6%
g45
 
7.6%
E33
 
5.6%
14
 
2.4%
T7
 
1.2%
Other values (6)42
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter479
80.9%
Uppercase Letter99
 
16.7%
Space Separator14
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n175
36.5%
d73
15.2%
e61
 
12.7%
i52
 
10.9%
u45
 
9.4%
g45
 
9.4%
o7
 
1.5%
t7
 
1.5%
r7
 
1.5%
m7
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
R45
45.5%
E33
33.3%
T7
 
7.1%
B7
 
7.1%
D7
 
7.1%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin578
97.6%
Common14
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n175
30.3%
d73
12.6%
e61
 
10.6%
i52
 
9.0%
R45
 
7.8%
u45
 
7.8%
g45
 
7.8%
E33
 
5.7%
T7
 
1.2%
o7
 
1.2%
Other values (5)35
 
6.1%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n175
29.6%
d73
12.3%
e61
 
10.3%
i52
 
8.8%
R45
 
7.6%
u45
 
7.6%
g45
 
7.6%
E33
 
5.6%
14
 
2.4%
T7
 
1.2%
Other values (6)42
 
7.1%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)28.6%
Missing22
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean38.3015873
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:30.585491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.7
Q124
median30
Q345
95-th percentile90
Maximum120
Range118
Interquartile range (IQR)21

Descriptive statistics

Standard deviation25.43406081
Coefficient of variation (CV)0.6640471739
Kurtosis3.536184517
Mean38.3015873
Median Absolute Deviation (MAD)15
Skewness1.682247771
Sum2413
Variance646.8914491
MonotonicityNot monotonic
2022-05-09T21:05:30.878052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4515
17.6%
3014
16.5%
205
 
5.9%
254
 
4.7%
604
 
4.7%
123
 
3.5%
1203
 
3.5%
152
 
2.4%
52
 
2.4%
432
 
2.4%
Other values (8)9
10.6%
(Missing)22
25.9%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
52
 
2.4%
123
 
3.5%
152
 
2.4%
191
 
1.2%
205
 
5.9%
231
 
1.2%
254
 
4.7%
3014
16.5%
ValueCountFrequency (%)
1203
 
3.5%
902
 
2.4%
604
 
4.7%
551
 
1.2%
4515
17.6%
432
 
2.4%
401
 
1.2%
331
 
1.2%
3014
16.5%
254
 
4.7%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)40.2%
Missing3
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean36.02439024
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:31.214134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q120
median31
Q345
95-th percentile89.25
Maximum120
Range118
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.58041758
Coefficient of variation (CV)0.6823270961
Kurtosis2.817727545
Mean36.02439024
Median Absolute Deviation (MAD)14
Skewness1.410234667
Sum2954
Variance604.1969286
MonotonicityNot monotonic
2022-05-09T21:05:31.644779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4514
16.5%
307
 
8.2%
315
 
5.9%
205
 
5.9%
254
 
4.7%
544
 
4.7%
54
 
4.7%
124
 
4.7%
604
 
4.7%
433
 
3.5%
Other values (23)28
32.9%
(Missing)3
 
3.5%
ValueCountFrequency (%)
21
 
1.2%
41
 
1.2%
54
4.7%
61
 
1.2%
71
 
1.2%
102
2.4%
124
4.7%
131
 
1.2%
141
 
1.2%
152
2.4%
ValueCountFrequency (%)
1202
 
2.4%
1101
 
1.2%
911
 
1.2%
901
 
1.2%
751
 
1.2%
604
 
4.7%
591
 
1.2%
544
 
4.7%
4514
16.5%
433
 
3.5%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size808.0 B
2020-12-09
12 
2020-11-23
 
4
2019-11-18
 
4
2020-12-08
 
3
2020-11-18
 
3
Other values (52)
59 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters850
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)52.9%

Sample

1st row2019-04-03
2nd row2020-11-30
3rd row2020-12-09
4th row2020-12-09
5th row2020-04-14

Common Values

ValueCountFrequency (%)
2020-12-0912
 
14.1%
2020-11-234
 
4.7%
2019-11-184
 
4.7%
2020-12-083
 
3.5%
2020-11-183
 
3.5%
2020-07-082
 
2.4%
2020-11-192
 
2.4%
2013-12-242
 
2.4%
2020-11-042
 
2.4%
2020-12-022
 
2.4%
Other values (47)49
57.6%

Length

2022-05-09T21:05:32.107389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-0912
 
14.1%
2019-11-184
 
4.7%
2020-11-234
 
4.7%
2020-12-083
 
3.5%
2020-11-183
 
3.5%
2020-11-042
 
2.4%
2020-11-242
 
2.4%
2020-12-022
 
2.4%
2020-11-302
 
2.4%
2013-12-242
 
2.4%
Other values (47)49
57.6%

Most occurring characters

ValueCountFrequency (%)
0217
25.5%
2187
22.0%
-170
20.0%
1144
16.9%
941
 
4.8%
825
 
2.9%
421
 
2.5%
316
 
1.9%
712
 
1.4%
69
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number680
80.0%
Dash Punctuation170
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0217
31.9%
2187
27.5%
1144
21.2%
941
 
6.0%
825
 
3.7%
421
 
3.1%
316
 
2.4%
712
 
1.8%
69
 
1.3%
58
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0217
25.5%
2187
22.0%
-170
20.0%
1144
16.9%
941
 
4.8%
825
 
2.9%
421
 
2.5%
316
 
1.9%
712
 
1.4%
69
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0217
25.5%
2187
22.0%
-170
20.0%
1144
16.9%
941
 
4.8%
825
 
2.9%
421
 
2.5%
316
 
1.9%
712
 
1.4%
69
 
1.1%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
52 
2020-12-09
2020-12-16
 
5
2020-12-30
 
5
2021-01-27
 
2
Other values (10)
13 

Length

Max length10
Median length3
Mean length5.717647059
Min length3

Characters and Unicode

Total characters486
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.2%

Sample

1st rownan
2nd row2020-12-11
3rd row2020-12-16
4th row2020-12-16
5th row2020-12-10

Common Values

ValueCountFrequency (%)
nan52
61.2%
2020-12-098
 
9.4%
2020-12-165
 
5.9%
2020-12-305
 
5.9%
2021-01-272
 
2.4%
2020-12-232
 
2.4%
2021-01-052
 
2.4%
2021-01-142
 
2.4%
2020-12-111
 
1.2%
2020-12-101
 
1.2%
Other values (5)5
 
5.9%

Length

2022-05-09T21:05:32.457761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan52
61.2%
2020-12-098
 
9.4%
2020-12-165
 
5.9%
2020-12-305
 
5.9%
2021-01-272
 
2.4%
2020-12-232
 
2.4%
2021-01-052
 
2.4%
2021-01-142
 
2.4%
2020-12-111
 
1.2%
2020-12-101
 
1.2%
Other values (5)5
 
5.9%

Most occurring characters

ValueCountFrequency (%)
n104
21.4%
298
20.2%
084
17.3%
-66
13.6%
a52
10.7%
151
10.5%
39
 
1.9%
98
 
1.6%
66
 
1.2%
43
 
0.6%
Other values (3)5
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264
54.3%
Lowercase Letter156
32.1%
Dash Punctuation66
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
298
37.1%
084
31.8%
151
19.3%
39
 
3.4%
98
 
3.0%
66
 
2.3%
43
 
1.1%
72
 
0.8%
52
 
0.8%
81
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n104
66.7%
a52
33.3%
Dash Punctuation
ValueCountFrequency (%)
-66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common330
67.9%
Latin156
32.1%

Most frequent character per script

Common
ValueCountFrequency (%)
298
29.7%
084
25.5%
-66
20.0%
151
15.5%
39
 
2.7%
98
 
2.4%
66
 
1.8%
43
 
0.9%
72
 
0.6%
52
 
0.6%
Latin
ValueCountFrequency (%)
n104
66.7%
a52
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n104
21.4%
298
20.2%
084
17.3%
-66
13.6%
a52
10.7%
151
10.5%
39
 
1.9%
98
 
1.6%
66
 
1.2%
43
 
0.6%
Other values (3)5
 
1.0%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
11 
https://tv.nrk.no/serie/puckers
 
5
https://www.netflix.com/title/81004466
 
4
https://www.bbc.co.uk/programmes/m000bks0
 
4
https://www.iqiyi.com/a_19rrhskr95.html
 
2
Other values (52)
59 

Length

Max length250
Median length70
Mean length43.98823529
Min length3

Characters and Unicode

Total characters3739
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)52.9%

Sample

1st rowhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI
2nd rowhttp://epic-media.ru/project/kotiki
3rd rowhttps://www.ivi.ru/watch/mertvyie-dushi-2020
4th rowhttps://www.ivi.ru/watch/mertvyie-dushi-2020
5th rownan

Common Values

ValueCountFrequency (%)
nan11
 
12.9%
https://tv.nrk.no/serie/puckers5
 
5.9%
https://www.netflix.com/title/810044664
 
4.7%
https://www.bbc.co.uk/programmes/m000bks04
 
4.7%
https://www.iqiyi.com/a_19rrhskr95.html2
 
2.4%
https://www.ivi.ru/watch/mertvyie-dushi-20202
 
2.4%
https://tv.nrk.no/serie/gjoer-det-sjoel2
 
2.4%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.4%
https://v.qq.com/detail/m/mzc00200tu76tos.html2
 
2.4%
https://www.tytnetwork.com2
 
2.4%
Other values (47)49
57.6%

Length

2022-05-09T21:05:32.909478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan11
 
12.9%
https://tv.nrk.no/serie/puckers5
 
5.9%
https://www.netflix.com/title/810044664
 
4.7%
https://www.bbc.co.uk/programmes/m000bks04
 
4.7%
https://v.qq.com/detail/m/mzc00200tu76tos.html2
 
2.4%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.4%
https://www.tytnetwork.com2
 
2.4%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
2.4%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.4%
https://tv.nrk.no/serie/gjoer-det-sjoel2
 
2.4%
Other values (47)49
57.6%

Most occurring characters

ValueCountFrequency (%)
/305
 
8.2%
t282
 
7.5%
s187
 
5.0%
e177
 
4.7%
.151
 
4.0%
o146
 
3.9%
h136
 
3.6%
%131
 
3.5%
w127
 
3.4%
r125
 
3.3%
Other values (64)1972
52.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2357
63.0%
Other Punctuation677
 
18.1%
Decimal Number382
 
10.2%
Uppercase Letter248
 
6.6%
Dash Punctuation46
 
1.2%
Math Symbol16
 
0.4%
Connector Punctuation13
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t282
 
12.0%
s187
 
7.9%
e177
 
7.5%
o146
 
6.2%
h136
 
5.8%
w127
 
5.4%
r125
 
5.3%
n119
 
5.0%
p119
 
5.0%
i114
 
4.8%
Other values (16)825
35.0%
Uppercase Letter
ValueCountFrequency (%)
E48
19.4%
B47
19.0%
A23
 
9.3%
Y10
 
4.0%
C10
 
4.0%
Q9
 
3.6%
P8
 
3.2%
S8
 
3.2%
F7
 
2.8%
L7
 
2.8%
Other values (16)71
28.6%
Decimal Number
ValueCountFrequency (%)
088
23.0%
863
16.5%
943
11.3%
133
 
8.6%
432
 
8.4%
629
 
7.6%
528
 
7.3%
228
 
7.3%
721
 
5.5%
317
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/305
45.1%
.151
22.3%
%131
19.4%
:74
 
10.9%
?9
 
1.3%
&5
 
0.7%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=14
87.5%
+2
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-46
100.0%
Connector Punctuation
ValueCountFrequency (%)
_13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2605
69.7%
Common1134
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t282
 
10.8%
s187
 
7.2%
e177
 
6.8%
o146
 
5.6%
h136
 
5.2%
w127
 
4.9%
r125
 
4.8%
n119
 
4.6%
p119
 
4.6%
i114
 
4.4%
Other values (42)1073
41.2%
Common
ValueCountFrequency (%)
/305
26.9%
.151
13.3%
%131
11.6%
088
 
7.8%
:74
 
6.5%
863
 
5.6%
-46
 
4.1%
943
 
3.8%
133
 
2.9%
432
 
2.8%
Other values (12)168
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/305
 
8.2%
t282
 
7.5%
s187
 
5.0%
e177
 
4.7%
.151
 
4.0%
o146
 
3.9%
h136
 
3.6%
%131
 
3.5%
w127
 
3.4%
r125
 
3.3%
Other values (64)1972
52.7%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.96470588
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:33.239991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median20
Q340
95-th percentile75.6
Maximum88
Range87
Interquartile range (IQR)26

Descriptive statistics

Standard deviation21.77014505
Coefficient of variation (CV)0.778486466
Kurtosis0.5152503913
Mean27.96470588
Median Absolute Deviation (MAD)12
Skewness1.069235821
Sum2377
Variance473.9392157
MonotonicityNot monotonic
2022-05-09T21:05:33.479866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
189
 
10.6%
85
 
5.9%
435
 
5.9%
35
 
5.9%
174
 
4.7%
354
 
4.7%
293
 
3.5%
203
 
3.5%
63
 
3.5%
153
 
3.5%
Other values (32)41
48.2%
ValueCountFrequency (%)
11
 
1.2%
22
 
2.4%
35
5.9%
51
 
1.2%
63
3.5%
72
 
2.4%
85
5.9%
101
 
1.2%
111
 
1.2%
141
 
1.2%
ValueCountFrequency (%)
881
1.2%
861
1.2%
821
1.2%
772
2.4%
702
2.4%
691
1.2%
641
1.2%
571
1.2%
551
1.2%
531
1.2%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
83 
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
2

Length

Max length66
Median length3
Mean length4.482352941
Min length3

Characters and Unicode

Total characters381
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan83
97.6%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}2
 
2.4%

Length

2022-05-09T21:05:33.927270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:05:34.206532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan83
87.4%
name2
 
2.1%
ukraine2
 
2.1%
code2
 
2.1%
ua2
 
2.1%
timezone2
 
2.1%
europe/zaporozhye2
 
2.1%

Most occurring characters

ValueCountFrequency (%)
n172
45.1%
a89
23.4%
'24
 
6.3%
e14
 
3.7%
o10
 
2.6%
10
 
2.6%
:6
 
1.6%
r6
 
1.6%
i4
 
1.0%
p4
 
1.0%
Other values (17)42
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter321
84.3%
Other Punctuation36
 
9.4%
Space Separator10
 
2.6%
Uppercase Letter10
 
2.6%
Open Punctuation2
 
0.5%
Close Punctuation2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n172
53.6%
a89
27.7%
e14
 
4.4%
o10
 
3.1%
r6
 
1.9%
i4
 
1.2%
p4
 
1.2%
z4
 
1.2%
m4
 
1.2%
u2
 
0.6%
Other values (6)12
 
3.7%
Other Punctuation
ValueCountFrequency (%)
'24
66.7%
:6
 
16.7%
,4
 
11.1%
/2
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
U4
40.0%
Z2
20.0%
E2
20.0%
A2
20.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
{2
100.0%
Close Punctuation
ValueCountFrequency (%)
}2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin331
86.9%
Common50
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n172
52.0%
a89
26.9%
e14
 
4.2%
o10
 
3.0%
r6
 
1.8%
i4
 
1.2%
p4
 
1.2%
z4
 
1.2%
U4
 
1.2%
m4
 
1.2%
Other values (10)20
 
6.0%
Common
ValueCountFrequency (%)
'24
48.0%
10
20.0%
:6
 
12.0%
,4
 
8.0%
/2
 
4.0%
{2
 
4.0%
}2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n172
45.1%
a89
23.4%
'24
 
6.3%
e14
 
3.7%
o10
 
2.6%
10
 
2.6%
:6
 
1.6%
r6
 
1.6%
i4
 
1.0%
p4
 
1.0%
Other values (17)42
 
11.0%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size808.0 B
nan
12 
<p>Can a dab of vodka, a little rølp and a good dose of amateur hockey break down barriers and create friendships? The happy hockey amateurs in <b>Kirkenes Puckers</b> think so.</p>
 
5
<p><b>The Surgeon's Cut </b>profiles four ground-breaking surgeons from around the world, each with a visionary approach to their craft. Viewers will follow along as they perform innovative operations and procedures, and reveal personal insight into their journey into medicine, providing a unique window into the world of surgery. Through the individual stories of these experts, the series explores how our understanding of the human body is constantly being reinvented by new discoveries and techniques. Specialty areas featured include fetal medicine, neurosurgery, transplant surgery and cardiology. </p>
 
4
<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>
 
4
<p>Morten shows you how you can make something cool with what you have at home!</p>
 
2
Other values (49)
58 

Length

Max length807
Median length395
Mean length250.2588235
Min length3

Characters and Unicode

Total characters21272
Distinct characters89
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)47.1%

Sample

1st rownan
2nd rownan
3rd row<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>
4th row<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>
5th row<p><b>Mermaid Prince</b> is about Hye Ri, who embarks on a graduation trip to Gangwon Province and meets Woo Hyuk, a mysterious guy who runs a guest house.</p>

Common Values

ValueCountFrequency (%)
nan12
 
14.1%
<p>Can a dab of vodka, a little rølp and a good dose of amateur hockey break down barriers and create friendships? The happy hockey amateurs in <b>Kirkenes Puckers</b> think so.</p>5
 
5.9%
<p><b>The Surgeon's Cut </b>profiles four ground-breaking surgeons from around the world, each with a visionary approach to their craft. Viewers will follow along as they perform innovative operations and procedures, and reveal personal insight into their journey into medicine, providing a unique window into the world of surgery. Through the individual stories of these experts, the series explores how our understanding of the human body is constantly being reinvented by new discoveries and techniques. Specialty areas featured include fetal medicine, neurosurgery, transplant surgery and cardiology. </p>4
 
4.7%
<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>4
 
4.7%
<p>Morten shows you how you can make something cool with what you have at home!</p>2
 
2.4%
<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>2
 
2.4%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.4%
<p>Pan, a desolate plastic surgeon, lived a repetitive and boring life every day until a conspiracy happened. He woke up in an abandoned factory, and found that someone had replaced his identity with a face exactly like him. His world has been completely overturned and left in a perilous situation. Can he overcome the difficulties and peel away the truth? How would he regain his identity?</p>2
 
2.4%
<p>Merchant Jiang Shuo and his odd specialist companion Qin Yi Heng purchase frequented houses to exchange them. In any case, alarming things start to occur and each spooky house is by all accounts part of a major riddle. Jiang Shuo, Yi Heng, and police officer Yuan Mu Qing attempt to understand the riddle.</p>2
 
2.4%
<p>A story that follows people whose lives are entangled due to a complicated case. While investigating a drug cartel as an undercover cop, Yan Jin falls in love with the beautiful coffee shop owner Ji Xiao'ou.</p>2
 
2.4%
Other values (44)48
56.5%

Length

2022-05-09T21:05:34.571259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the150
 
4.3%
and141
 
4.0%
a126
 
3.6%
to96
 
2.7%
of88
 
2.5%
in62
 
1.8%
his50
 
1.4%
with48
 
1.4%
he35
 
1.0%
is26
 
0.7%
Other values (1185)2674
76.5%

Most occurring characters

ValueCountFrequency (%)
3405
16.0%
e1909
 
9.0%
a1359
 
6.4%
n1307
 
6.1%
o1240
 
5.8%
i1220
 
5.7%
t1212
 
5.7%
s1029
 
4.8%
r1004
 
4.7%
h777
 
3.7%
Other values (79)6810
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16108
75.7%
Space Separator3412
 
16.0%
Uppercase Letter665
 
3.1%
Other Punctuation548
 
2.6%
Math Symbol416
 
2.0%
Dash Punctuation52
 
0.2%
Decimal Number44
 
0.2%
Format12
 
0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1909
11.9%
a1359
 
8.4%
n1307
 
8.1%
o1240
 
7.7%
i1220
 
7.6%
t1212
 
7.5%
s1029
 
6.4%
r1004
 
6.2%
h777
 
4.8%
l646
 
4.0%
Other values (20)4405
27.3%
Uppercase Letter
ValueCountFrequency (%)
S70
 
10.5%
T50
 
7.5%
M42
 
6.3%
W41
 
6.2%
H40
 
6.0%
C37
 
5.6%
A35
 
5.3%
Y30
 
4.5%
E27
 
4.1%
R27
 
4.1%
Other values (16)266
40.0%
Other Punctuation
ValueCountFrequency (%)
,176
32.1%
.174
31.8%
/114
20.8%
'37
 
6.8%
"20
 
3.6%
?13
 
2.4%
!5
 
0.9%
:4
 
0.7%
@1
 
0.2%
1
 
0.2%
Other values (3)3
 
0.5%
Decimal Number
ValueCountFrequency (%)
021
47.7%
26
 
13.6%
35
 
11.4%
15
 
11.4%
83
 
6.8%
72
 
4.5%
51
 
2.3%
41
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
-49
94.2%
2
 
3.8%
1
 
1.9%
Space Separator
ValueCountFrequency (%)
3405
99.8%
 7
 
0.2%
Math Symbol
ValueCountFrequency (%)
>208
50.0%
<208
50.0%
Currency Symbol
ValueCountFrequency (%)
$2
66.7%
1
33.3%
Format
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16773
78.9%
Common4499
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1909
 
11.4%
a1359
 
8.1%
n1307
 
7.8%
o1240
 
7.4%
i1220
 
7.3%
t1212
 
7.2%
s1029
 
6.1%
r1004
 
6.0%
h777
 
4.6%
l646
 
3.9%
Other values (46)5070
30.2%
Common
ValueCountFrequency (%)
3405
75.7%
>208
 
4.6%
<208
 
4.6%
,176
 
3.9%
.174
 
3.9%
/114
 
2.5%
-49
 
1.1%
'37
 
0.8%
021
 
0.5%
"20
 
0.4%
Other values (23)87
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII21240
99.8%
Punctuation16
 
0.1%
None15
 
0.1%
Currency Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3405
16.0%
e1909
 
9.0%
a1359
 
6.4%
n1307
 
6.2%
o1240
 
5.8%
i1220
 
5.7%
t1212
 
5.7%
s1029
 
4.8%
r1004
 
4.7%
h777
 
3.7%
Other values (69)6778
31.9%
Punctuation
ValueCountFrequency (%)
12
75.0%
2
 
12.5%
1
 
6.2%
1
 
6.2%
None
ValueCountFrequency (%)
 7
46.7%
ø5
33.3%
æ1
 
6.7%
å1
 
6.7%
é1
 
6.7%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1633126620
Minimum1606418164
Maximum1651933962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size808.0 B
2022-05-09T21:05:34.997728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1606418164
5-th percentile1608403625
Q11612842583
median1639237512
Q31649438482
95-th percentile1651686870
Maximum1651933962
Range45515798
Interquartile range (IQR)36595899

Descriptive statistics

Standard deviation17377467.18
Coefficient of variation (CV)0.01064061228
Kurtosis-1.620518955
Mean1633126620
Median Absolute Deviation (MAD)11671288
Skewness-0.3818767855
Sum1.388157627 × 1011
Variance3.019763655 × 1014
MonotonicityNot monotonic
2022-05-09T21:05:35.454139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16090060745
 
5.9%
16470520164
 
4.7%
16497929814
 
4.7%
16357351792
 
2.4%
16508264802
 
2.4%
16481900582
 
2.4%
16196334992
 
2.4%
16128425832
 
2.4%
16090607262
 
2.4%
16341681392
 
2.4%
Other values (55)58
68.2%
ValueCountFrequency (%)
16064181642
 
2.4%
16075487681
 
1.2%
16082530132
 
2.4%
16090060745
5.9%
16090607262
 
2.4%
16095351412
 
2.4%
16102051551
 
1.2%
16108125261
 
1.2%
16108903401
 
1.2%
16114368421
 
1.2%
ValueCountFrequency (%)
16519339621
1.2%
16519332091
1.2%
16518386471
1.2%
16517773161
1.2%
16516880411
1.2%
16516821881
1.2%
16516456841
1.2%
16512600631
1.2%
16512533561
1.2%
16512343741
1.2%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/1950369
 
1
https://api.tvmaze.com/episodes/1998678
 
1
https://api.tvmaze.com/episodes/1998676
 
1
https://api.tvmaze.com/episodes/1998675
 
1
Other values (80)
80 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3315
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/19503691
 
1.2%
https://api.tvmaze.com/episodes/19986781
 
1.2%
https://api.tvmaze.com/episodes/19986761
 
1.2%
https://api.tvmaze.com/episodes/19986751
 
1.2%
https://api.tvmaze.com/episodes/19986741
 
1.2%
https://api.tvmaze.com/episodes/19986731
 
1.2%
https://api.tvmaze.com/episodes/19978151
 
1.2%
https://api.tvmaze.com/episodes/19978141
 
1.2%
https://api.tvmaze.com/episodes/20833311
 
1.2%
Other values (75)75
88.2%

Length

2022-05-09T21:05:35.988379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/20158371
 
1.2%
https://api.tvmaze.com/episodes/19640001
 
1.2%
https://api.tvmaze.com/episodes/19954051
 
1.2%
https://api.tvmaze.com/episodes/20077601
 
1.2%
https://api.tvmaze.com/episodes/19857891
 
1.2%
https://api.tvmaze.com/episodes/20396221
 
1.2%
https://api.tvmaze.com/episodes/20396231
 
1.2%
https://api.tvmaze.com/episodes/23244271
 
1.2%
https://api.tvmaze.com/episodes/23244281
 
1.2%
Other values (75)75
88.2%

Most occurring characters

ValueCountFrequency (%)
/340
 
10.3%
p255
 
7.7%
s255
 
7.7%
e255
 
7.7%
t255
 
7.7%
o170
 
5.1%
a170
 
5.1%
i170
 
5.1%
.170
 
5.1%
m170
 
5.1%
Other values (16)1105
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2125
64.1%
Other Punctuation595
 
17.9%
Decimal Number595
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p255
12.0%
s255
12.0%
e255
12.0%
t255
12.0%
o170
8.0%
a170
8.0%
i170
8.0%
m170
8.0%
h85
 
4.0%
d85
 
4.0%
Other values (3)255
12.0%
Decimal Number
ValueCountFrequency (%)
9108
18.2%
292
15.5%
180
13.4%
356
9.4%
054
9.1%
850
8.4%
643
 
7.2%
441
 
6.9%
737
 
6.2%
534
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/340
57.1%
.170
28.6%
:85
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2125
64.1%
Common1190
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/340
28.6%
.170
14.3%
9108
 
9.1%
292
 
7.7%
:85
 
7.1%
180
 
6.7%
356
 
4.7%
054
 
4.5%
850
 
4.2%
643
 
3.6%
Other values (3)112
 
9.4%
Latin
ValueCountFrequency (%)
p255
12.0%
s255
12.0%
e255
12.0%
t255
12.0%
o170
8.0%
a170
8.0%
i170
8.0%
m170
8.0%
h85
 
4.0%
d85
 
4.0%
Other values (3)255
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/340
 
10.3%
p255
 
7.7%
s255
 
7.7%
e255
 
7.7%
t255
 
7.7%
o170
 
5.1%
a170
 
5.1%
i170
 
5.1%
.170
 
5.1%
m170
 
5.1%
Other values (16)1105
33.3%

Interactions

2022-05-09T21:05:16.540342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:54.114834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:59.984051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:02.201680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:04.157671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:06.009972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:09.525111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:11.148377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:13.022612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:19.000888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:55.173911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:00.931029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:02.916923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:04.860946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:06.924472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.066920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:11.869004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:14.048969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:19.285471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:55.589395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:01.047790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.017539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:04.961666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:07.324082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.172957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:11.968885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:14.193439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:19.710325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:55.992805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:01.157149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.117567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.058597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:07.597260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.277637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.061116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:14.324926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:20.059828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:56.673071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:01.274327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.309934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.155594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:07.863550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.386629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.156819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:14.442422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:22.017570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:58.235882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:01.820289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.792969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.642606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:08.544382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.726944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.651830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:15.675279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:22.234634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:58.651484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:01.917050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.886707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.737117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:08.716237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.826688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.750135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:15.856395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:22.407813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:59.177325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:02.014116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:03.978030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.834564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:08.976131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:10.933337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.843885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:16.079671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:22.585546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:04:59.543746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:02.106170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:04.070656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:05.920528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:09.257470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:11.028007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:12.931504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:05:16.298229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:05:36.392913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:05:37.025695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:05:37.481570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:05:37.894684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:05:38.577915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:05:22.894297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:05:23.895735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:05:24.199700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:05:24.365201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
02179612https://www.tvmaze.com/episodes/2179612/kontakty-1x29-kontakty-v-telefone-nikolaa-soboleva-klava-koka-eldar-dzarahov-andrej-malahov-edvard-bilКОНТАКТЫ в телефоне Николая Соболева: Клава Кока, Эльдар Джарахов, Андрей Малахов, Эдвард Бил1.029.0regular2020-12-0912:002020-12-09T00:00:00+00:0046.0nan49630https://www.tvmaze.com/shows/49630/kontaktyКонтактыGame ShowRussian[]Running30.041.02019-04-03nanhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI53.0nannan1.651688e+09https://api.tvmaze.com/episodes/1977902
11986871https://www.tvmaze.com/episodes/1986871/kotiki-1x08-seria-8Серия 81.08.0regular2020-12-09nan2020-12-09T00:00:00+00:0013.0nan52198https://www.tvmaze.com/shows/52198/kotikiКотикиScriptedRussian['Comedy']Ended12.012.02020-11-302020-12-11http://epic-media.ru/project/kotiki15.0nannan1.637555e+09https://api.tvmaze.com/episodes/2015818
21983257https://www.tvmaze.com/episodes/1983257/mertvye-dusi-1x01-seria-1Серия 11.01.0regular2020-12-09nan2020-12-09T00:00:00+00:0043.0nan52316https://www.tvmaze.com/shows/52316/mertvye-dusiМёртвые душиScriptedRussian['Comedy']Ended43.043.02020-12-092020-12-16https://www.ivi.ru/watch/mertvyie-dushi-202018.0nan<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>1.608253e+09https://api.tvmaze.com/episodes/1964000
31983258https://www.tvmaze.com/episodes/1983258/mertvye-dusi-1x02-seria-2Серия 21.02.0regular2020-12-09nan2020-12-09T00:00:00+00:0043.0nan52316https://www.tvmaze.com/shows/52316/mertvye-dusiМёртвые душиScriptedRussian['Comedy']Ended43.043.02020-12-092020-12-16https://www.ivi.ru/watch/mertvyie-dushi-202018.0nan<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>1.608253e+09https://api.tvmaze.com/episodes/1995405
41971569https://www.tvmaze.com/episodes/1971569/mermaid-prince-2x09-episode-9Episode 92.09.0regular2020-12-0911:002020-12-09T02:00:00+00:0015.0nan47207https://www.tvmaze.com/shows/47207/mermaid-princeMermaid PrinceScriptedKorean['Drama', 'Romance', 'Mystery']Ended15.015.02020-04-142020-12-10nan34.0nan<p><b>Mermaid Prince</b> is about Hye Ri, who embarks on a graduation trip to Gangwon Province and meets Woo Hyuk, a mysterious guy who runs a guest house.</p>1.610205e+09https://api.tvmaze.com/episodes/2007760
51985044https://www.tvmaze.com/episodes/1985044/wan-sheng-jie-2x11-enough-money-was-left-for-this-episodeEnough money was left for this episode2.011.0regular2020-12-0910:002020-12-09T02:00:00+00:004.0nan48395https://www.tvmaze.com/shows/48395/wan-sheng-jieWan Sheng JieAnimationChinese['Comedy', 'Anime', 'Supernatural']Running4.04.02020-04-01nanhttps://v.qq.com/detail/a/awnia0n2erqryf3.html17.0nan<p>The pure and cute little devil Nini lives in Room 1031 of Wan Sheng Street Apartment. His roommates are no ordinary people: The lazy and wacky vampire Ira who spends most of his time gaming or watching TV, the unlucky dancer werewolf Vladimir, the serious and old-fashioned angel landlord Lynn and his naive little sister Lily, and many more.<br /><br />What none of them realize is that the power of the demon king is sleeping inside of Nini. With evil forces in hot pursuit, can Nini and his friends head off disaster?</p>1.647194e+09https://api.tvmaze.com/episodes/1985789
61985616https://www.tvmaze.com/episodes/1985616/yi-nian-yong-heng-1x20-episode-20Episode 201.020.0regular2020-12-0910:002020-12-09T02:00:00+00:0019.0nan49652https://www.tvmaze.com/shows/49652/yi-nian-yong-hengYi Nian Yong HengAnimationChinese['Comedy', 'Action', 'Anime', 'Fantasy']Running19.019.02020-08-12nanhttps://v.qq.com/detail/w/ww18u675tfmhas6.html17.0nan<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>1.649494e+09https://api.tvmaze.com/episodes/2039622
72096296https://www.tvmaze.com/episodes/2096296/no-turning-back-romance-1x02-221.02.0regular2020-12-09nan2020-12-09T03:00:00+00:0012.0nan55002https://www.tvmaze.com/shows/55002/no-turning-back-romanceNo Turning Back RomanceScriptedKorean[]EndedNaN12.02020-12-082021-01-06nan20.0nan<p>A teen romance of So Dam, a sixteen-year-old girl, who has never dated before but she receives her first-ever love confession from a mysterious boy. She is looking for the boy who secretly confessed to her while she was asleep on her desk. The clues include a male voice, mango fruit scent and gym uniform. She must piece the puzzle to find that person among the likely candidates that include hot shots Park Ji Hoo, Jeong Han Kyul, and Joo In Hyuk.</p>1.621617e+09https://api.tvmaze.com/episodes/2039623
82030019https://www.tvmaze.com/episodes/2030019/dolls-frontline-2x11-episode-11Episode 112.011.0regular2020-12-0912:002020-12-09T04:00:00+00:005.0nan45713https://www.tvmaze.com/shows/45713/dolls-frontlineDolls' FrontlineAnimationChinese['Comedy', 'Anime', 'Science-Fiction']Ended5.05.02019-07-282020-12-16https://www.bilibili.com/bangumi/media/md2822989520.0nan<p>Re-imagines famous firearms as moe girls with machine bodies that are known as T-Dolls.</p>1.613087e+09https://api.tvmaze.com/episodes/2324427
92066368https://www.tvmaze.com/episodes/2066368/chu-feng-yi-dian-shizi-1x05-episode-5Episode 51.05.0regular2020-12-09nan2020-12-09T04:00:00+00:0030.0nan54637https://www.tvmaze.com/shows/54637/chu-feng-yi-dian-shiziChu Feng: Yi Dian ShiziAnimationChinese['Action']Ended30.030.02020-11-182021-01-27http://weibo.com/u/65161794478.0nan<p>Ordinary high school student Haoxuan Sun was taken hostage in a seemingly robbery and rescued by a girl wearing winged battledress with bee bionics designs. Haoxuan Sun's peaceful life was stirred by the girl – human bioengineering weapon Vanguard Liuli. Discovered that he is the "Son of Eden" wanted by all the great powers, Haoxuan Sun and Liuli have been searching for the truth and fight against the so-called destiny. At the same time, people around Haoxuan Sun, senpai Ye Bai who he has a crash on, his best friend, and many others, were discovered to have a second identities.<br /><br />The new BEE anime is based on the original manga plot. In addition, BEE manga's author Baimao personally joined the production team. The new BEE will also include two subplots entirely new to the viewers.</p>1.617984e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
752135010https://www.tvmaze.com/episodes/2135010/dimension-20-7x05-trouble-at-the-tunnelTrouble at the Tunnel7.05.0regular2020-12-09nan2020-12-09T17:00:00+00:00NaNnan56531https://www.tvmaze.com/shows/56531/dimension-20Dimension 20Game ShowEnglish['Comedy', 'Adventure', 'Fantasy']RunningNaNNaN2018-09-12nanhttps://www.dropout.tv/dimension-2077.0nan<p>Heed the call of adventure and enter <b>Dimension 20</b> where Game Master Brennan Lee Mulligan, joined by comedians and pro gamers, blends comedy with tabletop RPGs.</p>1.651253e+09https://api.tvmaze.com/episodes/1949334
762014651https://www.tvmaze.com/episodes/2014651/0-z-majklom-surom-s05-special-referendum-potriben-vin-komus-ci-se-z-maksom-serbinouРеферендум! Потрібен він комусь чи? ЩЕ з Максом Щербиною5.0NaNinsignificant_special2020-12-0920:302020-12-09T18:30:00+00:0018.0nan22893https://www.tvmaze.com/shows/22893/0-z-majklom-surom#@)₴?$0 з Майклом ЩуромNewsUkrainian['Comedy', 'Family']Running30.030.02016-11-06nanhttp://1tv.com.ua/programs/maikl_shcur70.0{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}<p>Humorous and analytical show <b>#@₴?$0 with Michael Schur</b> proves that sometimes reality is impossible to joke. But the restless Michael Rat does the impossible and continues to work with a team of analysts to find the unnoticed in the important and the important in the unnoticed. Although sometimes he finds the completely unimportant in no one needs.</p>1.651260e+09https://api.tvmaze.com/episodes/1949335
771960033https://www.tvmaze.com/episodes/1960033/goede-tijden-slechte-tijden-31x59-aflevering-6314Aflevering 631431.059.0regular2020-12-0920:002020-12-09T19:00:00+00:0023.0nan2504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.025.01990-10-01nanhttp://gtst.nl/#!/77.0nannan1.651839e+09https://api.tvmaze.com/episodes/1949336
782053421https://www.tvmaze.com/episodes/2053421/canal-boat-diaries-2x01-ellesmere-port-to-audlemEllesmere Port to Audlem2.01.0regular2020-12-0919:302020-12-09T19:30:00+00:0030.0<p>Robbie battles his way through blanket weed on the Shropshire Union Canal and discovers industrial secrets in Audlem, Cheshire.</p>45155https://www.tvmaze.com/shows/45155/canal-boat-diariesCanal Boat DiariesDocumentaryEnglish['Travel']Running30.030.02019-11-18nanhttps://www.bbc.co.uk/programmes/m000bks035.0nan<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>1.647052e+09https://api.tvmaze.com/episodes/1996786
792053422https://www.tvmaze.com/episodes/2053422/canal-boat-diaries-2x02-market-drayton-to-stourport-on-severnMarket Drayton to Stourport-on-Severn2.02.0regular2020-12-0919:302020-12-09T19:30:00+00:0030.0<p>Robbie gets stuck in the mud in Woodseaves Cutting and explores the charming canal-side village of Kinver in Staffordshire.</p>45155https://www.tvmaze.com/shows/45155/canal-boat-diariesCanal Boat DiariesDocumentaryEnglish['Travel']Running30.030.02019-11-18nanhttps://www.bbc.co.uk/programmes/m000bks035.0nan<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>1.647052e+09https://api.tvmaze.com/episodes/1955318
802053423https://www.tvmaze.com/episodes/2053423/canal-boat-diaries-2x03-stourport-basins-to-kingswood-junctionStourport Basins to Kingswood Junction2.03.0regular2020-12-0919:302020-12-09T19:30:00+00:0030.0<p>Robbie navigates the mighty River Severn and takes an unexpected bath as he takes a tumble at the Tardebigge lock flight in Worcestershire.</p>45155https://www.tvmaze.com/shows/45155/canal-boat-diariesCanal Boat DiariesDocumentaryEnglish['Travel']Running30.030.02019-11-18nanhttps://www.bbc.co.uk/programmes/m000bks035.0nan<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>1.647052e+09https://api.tvmaze.com/episodes/1996399
812053424https://www.tvmaze.com/episodes/2053424/canal-boat-diaries-2x04-stratford-upon-avon-to-birminghamStratford-upon-Avon to Birmingham2.04.0regular2020-12-0919:302020-12-09T19:30:00+00:0030.0<p>On the last leg of his journey across England, Robbie crosses an epic aqueduct near Stratford-upon-Avon and gets stuck in a lock in central Birmingham.</p>45155https://www.tvmaze.com/shows/45155/canal-boat-diariesCanal Boat DiariesDocumentaryEnglish['Travel']Running30.030.02019-11-18nanhttps://www.bbc.co.uk/programmes/m000bks035.0nan<p>Jump aboard with Robbie Cumming as he embarks on a 300-mile journey across the Midlands and northern England in his narrowboat.</p>1.647052e+09https://api.tvmaze.com/episodes/2042003
821958866https://www.tvmaze.com/episodes/1958866/wwe-nxt-14x50-main-event-raquel-gonzalez-vs-ember-moonMain Event: Raquel Gonzalez vs. Ember Moon14.050.0regular2020-12-0920:002020-12-10T01:00:00+00:00121.0nan2266https://www.tvmaze.com/shows/2266/wwe-nxtWWE NXTSportsEnglish[]Running120.075.02010-02-23nanhttp://www.wwe.com/inside/networkschedule88.0nan<p>Each Wednesday at 8:00 p.m. ET, WWE Superstars and Divas of tomorrow face off on <b>WWE NXT</b><i>,</i> a one-hour weekly show that features the brightest and best of WWE's rising stars. WWE NXT showcases the Superstars and Divas from WWE's Performance Center as well as appearances from WWE Superstars and Legends in an intimate setting. WWE NXT broadcasts from the state-of-the-art Full Sail LIVE venue on the Full Sail University in campus in Orlando, Florida.</p>1.651646e+09https://api.tvmaze.com/episodes/1996689
831976924https://www.tvmaze.com/episodes/1976924/ruth-ruby-virtual-sleepover-challenges-2020-12-09-holiday-gingerbread-triple-threatHoliday Gingerbread Triple Threat2020.09.0regular2020-12-0921:252020-12-10T02:25:00+00:007.0<p>Happy Holidays! BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World), Ruth Righi (Sydney to the Max), and special guest Izabela Rose (Upside-Down Magic) compete to see who can make the best gingerbread house! </p>45434https://www.tvmaze.com/shows/45434/ruth-ruby-virtual-sleepover-challengesRuth & Ruby Virtual Sleepover ChallengesTalk ShowEnglish['Comedy', 'Children', 'DIY']Running12.06.02019-08-09nanhttps://disneynow.com/shows/ruth-and-ruby-virtual-sleepover-challenges21.0nan<p>Grab your sleeping bag and join BFFs Ruby Rose Turner (Coop &amp; Cami Ask the World) and Ruth Righi (Sydney to the Max) for the ultimate sleepover!</p>1.639238e+09https://api.tvmaze.com/episodes/1950703
841945145https://www.tvmaze.com/episodes/1945145/noblesse-1x10-dangerous-man-loveparadeDangerous Man / LOVEPARADE1.010.0regular2020-12-0900:002020-12-10T05:00:00+00:0025.0<p>Rael appears at Ye Ran High School to pick up Seira, and attacks M-21, Takeo, and Tao. Rael, who was rejected by Seira, challenges Frankenstein to a battle. To protect his companions and to protect Raizel, Frankenstein can no longer keep silent.</p>49732https://www.tvmaze.com/shows/49732/noblesseNoblesseAnimationJapanese['Anime', 'Supernatural']Ended25.025.02015-12-042020-12-30https://noblesse-anime.com/44.0nan<p>Raizel awakens from his 820-year slumber. He holds the special title of Noblesse, a pure-blooded Noble and protector of all other Nobles. In an attempt to protect Raizel, his servant Frankenstein enrolls him at Ye Ran High School, where Raizel learns the simple and quotidian routines of the human world through his classmates. However, the Union, a secret society plotting to take over the world, dispatches modified humans and gradually encroaches on Raizel's life, causing him to wield his mighty power to protect those around him... After 820 years of intrigue, the secrets behind his slumber are finally revealed, and Raizel's absolute protection as the Noblesse begins!</p>1.648717e+09https://api.tvmaze.com/episodes/2050241